lu-group / sbinn
SBINN: Systems-biology informed neural network
☆25Updated 3 months ago
Related projects: ⓘ
- ☆36Updated 2 years ago
- Biologically-informed neural networks☆24Updated 3 years ago
- Physics-informed neural networks☆11Updated 3 years ago
- ☆19Updated 4 years ago
- Code repository for the paper "Learning partial differential equations for biological transport models from noisy spatiotemporal data"☆10Updated 5 years ago
- Pytorch implementation of the DeepMoD algorithm: [arXiv:1904.09406]☆31Updated 10 months ago
- ☆17Updated 3 years ago
- Stiff Neural Ordinary Differential Equations☆30Updated last year
- ETH Zürich AI in the Sciences and Engineering Master's course 2024☆19Updated last month
- ☆21Updated 3 years ago
- Benchmark for learning stiff problems using physics-informed machine learning☆10Updated 2 years ago
- Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics☆48Updated 2 years ago
- Physics-Informed Neural Networks Trained with Particle Swarm Optimization☆15Updated 2 years ago
- ☆23Updated 2 months ago
- Source code for deep learning-based reduced order models in cardiac electrophysiology. Available on doi.org/10.1371/journal.pone.0239416.☆14Updated last year
- ☆21Updated 2 years ago
- Resources about Machine Learning for solving PDEs.☆46Updated last year
- Solving Inverse Physics Problems with Score Matching☆19Updated 9 months ago
- ☆33Updated 3 years ago
- A Python library for training neural ODEs.☆19Updated last month
- Synthetic Lagrangian Turbulence by Generative Diffusion Models☆15Updated 6 months ago
- jupyter notebooks for the neural nets and differential equation paper☆27Updated 3 years ago
- Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning☆18Updated 8 months ago
- Sample codes of CNN-SINDy based reduced-order modeling for fluid flows by Fukami et al., JFM 2021.☆22Updated 3 years ago
- Low-dimensional PCA-derived manifolds and everything in between!☆12Updated 3 weeks ago
- PDE-VAE: Variational Autoencoder for Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning☆29Updated 2 years ago
- Source code for paper "Learning the Solution Operator of Boundary Value Problems using Graph Neural Networks"☆16Updated last month
- Convolutional Solvers for Partial Differential Equations☆28Updated 4 years ago
- ☆23Updated 10 months ago
- ☆19Updated last year